Cracks in the walls of a building are warnings of safety in structure. Without prompt reinforcement, even a tiny crack might broaden increasingly and expose the internal cramp iron, which will increase the possibility rusting and endanger the building.
Due to its geographical factors, Taiwan suffers disasters such as typhoons, flood, and earthquakes frequently. Thereby, buildings or constructions there have higher risks of generating cracks and then age and deteriorate gradually. This threatens people's lives and property seriously. Accordingly, when the maintenance department of constructions is maintaining and managing bridges, dams, tunnels, how to inspect and track the status of cracks firmly has become an important and unavoidable subject.
Traditionally, when cracks occur in concrete buildings, quantization of the length and width of the cracks is done by manual and contact measurement, namely, by using handheld crack gauges or ultrasonic inspection. Nonetheless, manual measurement is time consuming and labor intensive. Besides, the measurement results are not unique. Moreover, some locations of cracks are uneasy to reach, making it difficult for a large number of measurements.
In recent years, there are an increasing number of experts or scholars who use image recognition to extract crack information from images. However, although these methods according to the prior art can possibly give crack information, such as length and width, from images, the measurements are the numbers of pixels on images. Consequently, the real parameters of cracks can be given only after conversion via reference scales. Currently, there is still no simple and practical method for providing reference scales on images.